I encountered a similar issue when training the 1D convolutional autoencoder in MATLAB. It is due to a mismatch between the shape of the input data and what the ‘sequenceInputLayer’ expects. Specifically, ‘sequenceInputLayer(128)’ expects the input data to be a 3D array of the format [h, c, s] where h is the height (features), c is the number of channels, and s is the sequence length. Since the data ‘DATA_OUT’ and ‘DATA_IN’ are sized 128 x 1280, we need to reshape them to include the channel dimension before training. This can be done by using the ‘reshape’ function. The following code snippet shows how the issue can be resolved:
DATA_OUT_reshaped = reshape(DATA_OUT, [128, 1, 1280]);
DATA_IN_reshaped = reshape(DATA_IN, [128, 1, 1280]);
The following documentation link provides detailed information on the relevant topics:
- https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html
- https://www.mathworks.com/help/matlab/ref/double.reshape.html
- https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html#mw_36a68d96-8505-4b8d-b338-44e1efa9cc5e
I hope this helps!
